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Author Information
Aryan Mokhtari (MIT)
Asuman Ozdaglar (Massachusetts Institute of Technology)
Asu Ozdaglar received the B.S. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1996, and the S.M. and the Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2003, respectively. She is currently a professor in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology. She is also the director of the Laboratory for Information and Decision Systems. Her research expertise includes optimization theory, with emphasis on nonlinear programming and convex analysis, game theory, with applications in communication, social, and economic networks, distributed optimization and control, and network analysis with special emphasis on contagious processes, systemic risk and dynamic control. Professor Ozdaglar is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, the 2008 Donald P. Eckman award of the American Automatic Control Council, the Class of 1943 Career Development Chair, the inaugural Steven and Renee Innovation Fellowship, and the 2014 Spira teaching award. She served on the Board of Governors of the Control System Society in 2010 and was an associate editor for IEEE Transactions on Automatic Control. She is currently the area co-editor for a new area for the journal Operations Research, entitled "Games, Information and Networks. She is the co-author of the book entitled âConvex Analysis and Optimizationâ (Athena Scientific, 2003).
Ali Jadbabaie (MIT)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Spotlight: Escaping Saddle Points in Constrained Optimization »
Wed. Dec 5th 09:45 -- 09:50 PM Room Room 517 CD
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2022 : Smoothed-SGDmax: A Stability-Inspired Algorithm to Improve Adversarial Generalization »
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2023 Poster: Multi-Player Zero-Sum Markov Games with Networked Local Interactions »
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2023 Poster: Beyond Lipschitz Smoothness: A New Approach to Convex and Non-Convex Optimization »
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2022 Poster: What is a Good Metric to Study Generalization of Minimax Learners? »
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2021 : Q&A with Professor Asu Ozdaglar »
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2021 : Keynote Talk: Personalization in Federated Learning: Adaptation and Clustering (Asu Ozdaglar) »
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2021 Poster: Decentralized Q-learning in Zero-sum Markov Games »
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2021 Poster: Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks »
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2021 Poster: On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning »
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2020 Poster: Robust Federated Learning: The Case of Affine Distribution Shifts »
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2020 Poster: Estimation of Skill Distribution from a Tournament »
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2020 Spotlight: Estimation of Skill Distribution from a Tournament »
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2020 Poster: Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach »
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2019 Poster: Are deep ResNets provably better than linear predictors? »
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2019 Poster: Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity »
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2019 Spotlight: Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity »
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2019 Poster: A Universally Optimal Multistage Accelerated Stochastic Gradient Method »
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2018 Poster: Direct Runge-Kutta Discretization Achieves Acceleration »
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2018 Spotlight: Direct Runge-Kutta Discretization Achieves Acceleration »
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2017 Poster: When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent »
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2015 Invited Talk: Incremental Methods for Additive Cost Convex Optimization »
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2013 Poster: Computing the Stationary Distribution Locally »
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